{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T11:54:49.354350Z",
     "start_time": "2020-10-19T11:54:48.187085Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T12:10:15.030411Z",
     "start_time": "2020-10-19T12:10:15.023414Z"
    }
   },
   "outputs": [],
   "source": [
    "class Model(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model,self).__init__()\n",
    "        self.linear1=torch.nn.Linear(8,6)\n",
    "        self.linear2=torch.nn.Linear(6,3)\n",
    "        self.linear3=torch.nn.Linear(3,1)\n",
    "        self.sigmoid=torch.nn.Sigmoid()\n",
    "    def forward(self,x):\n",
    "        x=self.sigmoid(self.linear1(x))\n",
    "        x=self.sigmoid(self.linear2(x))\n",
    "        x=self.sigmoid(self.linear3(x))\n",
    "        return x\n",
    "model=Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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